# Convenient way to load list of packages
pkg_list <- c("sf", "terra", "ReLTER", "tmap")
lapply(pkg_list,require, character.only = TRUE)

## [[1]]
## [1] TRUE
## 
## [[2]]
## [1] TRUE
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] TRUE

tmap_options(check.and.fix = TRUE)
tmap_mode("view")

Package Maintainer

library(ReLTER)
maintainer("ReLTER")

## [1] "The package maintainer Alessandro Oggioni, phD (2020) <oggioniale@gmail.com>"

How to cite

citation("ReLTER")

## 
## To cite the 'ReLTER' package in publications use:
## 
##   Alessandro Oggioni, Micha Silver, Luigi Ranghetti & Paolo Tagliolato.
##   (2021). oggioniale/ReLTER: ReLTER v1.0.0 (1.0.0). Zenodo.
##   https://doi.org/10.5281/zenodo.5576813
## 
## A BibTeX entry for LaTeX users is
## 
##   @Misc{,
##     title = {oggioniale/ReLTER: ReLTER v1.0.0},
##     author = {Alessandro Oggioni and Micha Silver and Luigi Ranghetti and Paolo Tagliolato},
##     year = {2022},
##     note = {R package version v1.0.0},
##   }

Basic functions

ls("package:ReLTER")

##  [1] "%>%"                            "get_activity_info"             
##  [3] "get_dataset_info"               "get_ilter_envcharacts"         
##  [5] "get_ilter_generalinfo"          "get_ilter_parameters"          
##  [7] "get_ilter_research_topics"      "get_network_envcharacts"       
##  [9] "get_network_parameters"         "get_network_related_resources" 
## [11] "get_network_research_topics"    "get_network_sites"             
## [13] "get_site_info"                  "get_site_ODS"                  
## [15] "get_sos_procedurelist"          "produce_network_points_map"    
## [17] "produce_site_map"               "produce_site_parameters_pie"   
## [19] "produce_site_parameters_waffle" "produce_site_qrcode"           
## [21] "taxon_id_pesi"                  "taxon_id_worms"

Categories of functions

• site specific functions
• network functions
• metadata functions
• taxonomy functions

Search for DEIMS ID for a particular site. The function get_ilter_generalinfo allows to search by country name and site name. Then get_site_info() obtains various metadata about a chosen site.

For the first example, the Doñana LTSER Platform in Spain

donana = get_ilter_generalinfo(country_name = "Spain",
                              site_name = "Doñana")
(donana_id = donana$uri)

## [1] "https://deims.org/bcbc866c-3f4f-47a8-bbbc-0a93df6de7b2"

Plot a basic map of that site. We use the tmap package for viewing maps.

donana_polygon <- get_site_info(donana_id, category = "Boundaries")
tm_basemap("OpenStreetMap.Mapnik") + 
  tm_shape(donana_polygon) +
  tm_fill(col = "blue", alpha = 0.3)

Next example: Retrieve metadata about a site.

This example retrieves metadata from Lock Kinord in Scotland.

loch_kinord <- get_ilter_generalinfo(country_name = "United K",
                              site_name = "Loch Kinord")
(loch_kinord_id = loch_kinord$uri)

## [1] "https://deims.org/9fa171d2-5a24-40d3-9c06-b3f9e9d0f270"

loch_kinord_details <- get_site_info(loch_kinord_id,
                                 c("Contacts", "EnvCharacts", "Parameters"))

print(paste("Site manager:",
            loch_kinord_details$generalInfo.siteManager[[1]]['name'],
            loch_kinord_details$generalInfo.siteManager[[1]]['email']))

## [1] "Site manager: Andrew Sier [Primary ECN contact] arjs@ceh.ac.uk"

# Metadata contact:
(loch_kinord_details$generalInfo.metadataProvider[[1]]['name'])

##                                name
## 1 Andrew Sier [Primary ECN contact]
## 2                    Caroline Dilks

print(paste("Average air temperature:",
            loch_kinord_details$envCharacteristics.airTemperature.avg))

## [1] "Average air temperature: 6.62"

print(paste("Annual precipitation:",
            loch_kinord_details$envCharacteristics.precipitation.annual))

## [1] "Annual precipitation: 1031.3"

print(paste("GeoBonBiome:",
            loch_kinord_details$envCharacteristics.geoBonBiome[[1]]))

## [1] "GeoBonBiome: Fresh water lakes"

# Parameters:
head(loch_kinord_details$parameter[[1]]['parameterLabel'], 12)

##                       parameterLabel
## 1                   ammonium content
## 2    benthic invertebrates abundance
## 3     benthic invertebrates presence
## 4                       conductivity
## 5                 dissolved nutrient
## 6  dissolved organic carbon in water
## 7                ecosystem parameter
## 8         inorganic nutrient content
## 9                         lake level
## 10                  lake temperature
## 11                  nitrogen content
## 12                 species abundance

Next example: Query a network

The LTER network in Slovakia

lter_slovakia_id = "https://deims.org/networks/3d6a8d72-9f86-4082-ad56-a361b4cdc8a0"

network_research_topics <- get_network_research_topics(lter_slovakia_id)
head(network_research_topics$researchTopicsLabel, 20)

##  [1] "animal ecology"      "biodiversity"        "biology"            
##  [4] "climate change"      "climate monitoring"  "climatology"        
##  [7] "community dynamics"  "community ecology"   "ecology"            
## [10] "ecosystem ecology"   "ecosystem function"  "ecosystem service"  
## [13] "forest ecology"      "history"             "land use history"   
## [16] "meteorology"         "phenology"           "plant ecology"      
## [19] "population dynamics" "population ecology"

lter_slovakia_sites <- get_network_sites(lter_slovakia_id)
lter_slovakia_sites$title

## [1] "Bab - Slovakia"                                            
## [2] "Jalovecka dolina - Slovakia"                               
## [3] "Kralova hola - Slovakia"                                   
## [4] "Kremnicke vrchy Ecological Experimental Station - Slovakia"
## [5] "Polana Biosphere Reserve (Hukavsky grun) - Slovakia"       
## [6] "Poloniny National Park LTSER - Slovakia"                   
## [7] "Tatra National Park - Slovakia"                            
## [8] "Tatras - alpine summits - Slovakia"                        
## [9] "Trnava LTSER - Slovakia"

Show map of sites in the network

lter_slovakia <- produce_network_points_map(lter_slovakia_id, "SVK")
svk <- readRDS("gadm36_SVK_0_sp.rds")  # downloaded by produce_network_points_map()
tm_basemap("OpenStreetMap.Mapnik") + 
  tm_shape(lter_slovakia) + 
  tm_dots(col = "blue", size=0.04) +
  tm_shape(svk) + 
  tm_borders(col = "purple", lwd = 0.6) +
  tm_grid(alpha = 0.4) +
  tm_scale_bar(position = c("right", "bottom"))

pkg_list <- c("sf", "terra", "ReLTER", "tmap")
lapply(pkg_list, require, character.only = TRUE)

## [[1]]
## [1] TRUE
## 
## [[2]]
## [1] TRUE
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] TRUE

tmap_options(check.and.fix = TRUE)
tmap_mode("view")

ReLTER relies on the data entered into DEIMS-SDR. However sometimes there are:

• Multiple sites with similiar names
• Missing information
• Sites with no boundary polygon

First example, the Kiskun region of Hungary

Query for Site Manager

# Multiple sites in the KISKUN region of Hungary
kiskun <- get_ilter_generalinfo(country_name = "Hungary",
                              site_name = "KISKUN")
# How many sites?
print(paste("In Kiskun region: ", length(kiskun$title), "sites"))

## [1] "In Kiskun region:  8 sites"

(kiskun$title)

## [1] "Kiskun Forest Reserve Sites, KISKUN LTER - Hungary"   
## [2] "VULCAN Kiskunsag, KISKUN LTER - Hungary"              
## [3] "Kiskun Restoration Experiments, KISKUN LTER - Hungary"
## [4] "Kiskun Site Network (Jedlik), KISKUN LTER - Hungary"  
## [5] "KISKUN LTER - Hungary"                                
## [6] "LTER Fulophaza Site, KISKUN LTER - Hungary"           
## [7] "Bugac-Bocsa-Orgovany Site, KISKUN LTER - Hungary"     
## [8] "Orgovany Site, KISKUN LTER - Hungary"

# Which site? Bugac-Bocsa
bugac_id <- kiskun[7,]$uri
bugac_details <- get_site_info(bugac_id,"Contacts")
(bugac_details$generalInfo.siteManager[[1]]['name'])

##          name
## 1 Gábor Ónodi

Now query for boundary

bugac_polygon <- get_site_info(bugac_id, "Boundaries")

## 
## ----
## This site does not have boundaries uploaded to DEIMS-SDR.
## Please verify in the site page: https://deims.org/609e5959-8cd8-44a0-ab42-eda521cd452a
## ----

str(bugac_polygon)

## tibble [1 × 9] (S3: tbl_df/tbl/data.frame)
##  $ title       : chr "Bugac-Bocsa-Orgovany Site, KISKUN LTER - Hungary"
##  $ uri         : chr "https://deims.org/609e5959-8cd8-44a0-ab42-eda521cd452a"
##  $ boundaries  : logi NA
##  $ geoCoord    : chr "POINT (19.5281 46.7183)"
##  $ country     :List of 1
##   ..$ : chr "Hungary"
##  $ geoElev.avg : int 112
##  $ geoElev.min : int 105
##  $ geoElev.max : int 120
##  $ geoElev.unit: chr "msl"

# No geometry

• This site has the site manager’s name
• but no boundary polygon

Second example, Gran Paradiso in Italy

paradiso <- get_ilter_generalinfo(country_name = "Italy",
                              site_name = "Gran Paradiso")
(paradiso$title)

## [1] "IT23 - Gran Paradiso National Park - Italy"
## [2] "Gran Paradiso National Park - Italy"

# Choose the second
paradiso_id <- paradiso[2,]$uri
paradiso_details <- get_site_info(paradiso_id,"Contacts")
# Multiple names for metadata:
paradiso_details$generalInfo.metadataProvider[[1]]['name']

##                 name
## 1 Alessandro Oggioni
## 2     Ramona Viterbi

# But what about funding agency
paradiso_details$generalInfo.fundingAgency

## [1] NA

• This site has metadata providers
• but no funding agency

Functions within ReLTER help to acquire certain Earth Observation datasets. The get_site_ODS() function offers to ReLTER users access to the OpenDataScience Europe (ODS) archive (https://maps.opendatascience.eu/) with landcover, NDVI, natura2000, Corine landcover, and OSM features, all at 30 meter pixel resolution. Cropping to site boundaries is done in the cloud, and due to the Cloud Optimized Geotiff (COG) format, downloads are quite small.

First example, Kis-Balaton site in Kiskun region, Hungary

# Get DEIMS ID for Kis-Balaton site 
kis_balaton <- get_ilter_generalinfo(country_name = "Hungary",
                              site_name = "Kis-Balaton")
kb_id = kis_balaton$uri
kb_polygon = get_site_info(kb_id, "Boundaries")

# Now acquire landcover and NDVI from ODS
kb_landcover = get_site_ODS(kb_id, dataset = "landcover")
kb_ndvi_summer = get_site_ODS(kb_id, "ndvi_summer")

# Plot maps
tm_basemap("OpenStreetMap.Mapnik") + 
  tm_shape(kb_polygon) +
  tm_borders(col = "purple") + 
  tm_shape(kb_ndvi_summer) +
  tm_raster(alpha=0.7, palette = "RdYlGn")

tm_basemap("OpenStreetMap.Mapnik") + 
  tm_shape(kb_polygon) +
  tm_borders(col = "purple") + 
  tm_shape(kb_landcover) +
  tm_raster(alpha=0.7, palette = "Set1")

Second example, Companhia das Lezírias, Portugal

lezirias <- get_ilter_generalinfo(country_name = "Portugal",
                              site_name = "Companhia")
lezirias_id = lezirias$uri
lezirias_polygon = get_site_info(lezirias_id, "Boundaries")

# Now acquire spring NDVI from OSD
lezirias_ndvi_spring = get_site_ODS(lezirias_id, "ndvi_spring")

# Plot maps
tm_basemap("OpenStreetMap.Mapnik") + 
  tm_shape(lezirias_polygon) +
  tm_borders(col = "purple") + 
  tm_shape(lezirias_ndvi_spring) +
  tm_raster(alpha=0.7, palette = "RdYlGn")

The function outputs a raster. We can save to Geotiff for use in other GIS

class(lezirias_ndvi_spring)

## [1] "SpatRaster"
## attr(,"package")
## [1] "terra"

writeRaster(x = lezirias_ndvi_spring,
            filename = "lezirias_ndvi_spring.tif",
            overwrite = TRUE)

Environmental parameters

ReLTER has implemented some revealing visualizations of the various parameters collected at LTER sites. One visualization is the pie chart of environmental parameters.

In an example above the DEIMS ID of Kis Balaton (Kiskun LTER) was found. We’ll use that site to show a pie chart of environmental variables collected in that site.

produce_site_parameters_pie(kb_id)

## # A tibble: 8 × 9
##   parameterGroups            n   freq label   end start middle hjust vjust
##   <chr>                  <int>  <dbl> <chr> <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1 agricultural parameter     1 0.0208 2%    0.131 0     0.0654     0     0
## 2 atmospheric parameter      1 0.0208 2%    0.262 0.131 0.196      0     0
## 3 biological parameter      16 0.333  33%   2.36  0.262 1.31       0     0
## 4 chemical parameter        16 0.333  33%   4.45  2.36  3.40       1     1
## 5 ecosystem parameter        8 0.167  17%   5.50  4.45  4.97       1     0
## 6 physical parameter         1 0.0208 2%    5.63  5.50  5.56       1     0
## 7 soil parameter             1 0.0208 2%    5.76  5.63  5.69       1     0
## 8 water parameter            4 0.0833 8%    6.28  5.76  6.02       1     0

Similarly, a “waffle” chart can be produced.

produce_site_parameters_waffle(kb_id)

## # A tibble: 8 × 4
##   parameterGroups            n   freq label
##   <chr>                  <int>  <dbl> <chr>
## 1 agricultural parameter     1 0.0208 2%   
## 2 atmospheric parameter      1 0.0208 2%   
## 3 biological parameter      16 0.333  33%  
## 4 chemical parameter        16 0.333  33%  
## 5 ecosystem parameter        8 0.167  17%  
## 6 physical parameter         1 0.0208 2%   
## 7 soil parameter             1 0.0208 2%   
## 8 water parameter            4 0.0833 8%

Show a chaining of several functions

This example uses the LTER network in Greece. Call the produce_network_points_map() function (requires both DEIMS network ID and the three letter ISO code for the country to be mapped) to get all sites in a country.

lter_greece_id = "https://deims.org/networks/83453a6c-792d-4549-9dbb-c17ced2e0cc3"
lter_greece <- produce_network_points_map(lter_greece_id, "GRC")
grc <- readRDS("gadm36_GRC_0_sp.rds")  # available from `produce_network_points_map()

tm_basemap("OpenStreetMap.Mapnik") + 
  tm_shape(lter_greece) + 
  tm_dots(col = "blue", size=0.08) +
  tm_shape(grc) + 
  tm_borders(col = "purple", lwd=2) +
  tm_grid(alpha = 0.4) +
  tm_scale_bar(position = c("right", "bottom"))

pkg_list <- c("sf", "terra", "ReLTER", "tmap")
lapply(pkg_list, require, character.only = TRUE)

## [[1]]
## [1] TRUE
## 
## [[2]]
## [1] TRUE
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] TRUE

Each function returns an R object

• Site metadata and info returned as a tibble
  • save as an R dataset (keeps R stucture), or
  • save to csv file
• Boundaries are returned as an sf spatial vector layer.
  • save to a shapefile, or geopackage
• EO data are returned as a SpatRaster object (from the terra package)
  • save as geotiff

Example from the Doñana site in Spain

donana = get_ilter_generalinfo(country_name = "Spain",
                              site_name = "Doñana")
donana_id = donana$uri
donana_polygon <- get_site_info(donana_id, category = "Boundaries")
donana_meta <-  get_site_info(donana_id,
                          c("Affiliations", "Contacts", "Parameters"))
# Get the Corine landcover from ODS
donana_clc <- get_site_ODS(donana_id, "clc2018")

Save data and spatial layers for use later, or in other GIS software

# For this demo, save to temporary directory
output_dir = tempdir()
saveRDS(donana_meta, file.path(output_dir, "Donana_metadat.Rds"))
# Remove extra columns from polygon
donana_polygon <- donana_polygon[,c("title", "boundaries", "geoCoord", "geoElev.avg")]
st_write(donana_polygon,
         file.path(output_dir, "donana_polygon.gpkg"), append=FALSE)

## Writing layer `donana_polygon' to data source 
##   `/tmp/Rtmp8Jma3i/donana_polygon.gpkg' using driver `GPKG'
## Writing 1 features with 3 fields and geometry type Polygon.

writeRaster(donana_clc,
            file.path(output_dir, "Donana_Corine2018.tif"),
            overwrite=TRUE)

A Docker image for ReLTER package.

Capabilities

Run RStudio with preinstalled ReLTER package through Docker.

Quick setup

1. Follow the instructions to install Docker on your computer.
2. Open a terminal (“command prompt” in Windows) and write the following command to download the rocker_relter image

docker pull ptagliolato/rocker_relter

1. Run the image:

docker run -e PASSWORD=yourpassword -p 8080:8787 \
  ptagliolato/rocker_relter

* This will run RStudio on port 8080, you can change the port as you like.
* You can change "yourpassword" with the password you prefer.
* If you close the terminal, the image will stop. To avoid this, add "-d" before "-e" 
to run "detached" from the terminal.
* See further options in the last section.

Open your browser at localhost:8080 and login with

  user: rstudio  
  password: yourpassword

Run experimental features of ReLTER package

ReLTER package is evolving. You can run some of the features recently added to the package (namely those presented in eLTER Plus deliverable D.4.1 to retrieve and manage biodiversity data from GBIF, OBIS and iNaturalist) by using the alternative image "dev__withImprovements":

docker pull ptagliolato/rocker_relter:dev__withImprovements
    
docker run -e PASSWORD=yourpassword -p 8080:8787 \                                    
  ptagliolato/rocker_relter:dev__withImprovements

Preserve your work (use a docker volume)

When you stop a docker container, the files created within it are lost. In order to preserve your work across different runs, link a local volume (in this example, the current working directory, $(pwd)) to the container:

docker run -d -v $(pwd):/home/rstudio -e PASSWORD=yourpassword \
  -p 8080:8787 ptagliolato/rocker_relter

The site boundary, obtained from https://DEIMS.org can be used to crop and mask EO data from other sources. In this section we show use of global EO data acquired from a new R package geodata. Elevation from the Shuttle Radar Terrain Mission (SRTM) and species distribution from Global Biodiversity Information Facility (GBIF) will be downloaded, cropped to a site boundary and compared. The Gran Paradiso National Park will be used, and this example will examine Capra ibex sitings

Get Gran Paradiso boundary

paradiso <- get_ilter_generalinfo(country_name = "Italy",
                                site_name = "Gran Paradiso")
paradiso_id <- paradiso[2,]$uri
paradiso_boundary <- get_site_info(paradiso_id, "Boundaries")

Install geodata package and download SRTM over Northern Italy

# Get the geodata package
if (!require(geodata)) {
  install.packages("geodata", dependencies = TRUE)
  library(geodata)
}

## Loading required package: geodata

italy_srtm <- elevation_3s(lon = 7.5, lat = 47.5, path = tempdir())
paradiso_srtm <- mask(crop(italy_srtm, vect(paradiso_boundary)),
                      vect(paradiso_boundary))
names(paradiso_srtm) <- "Elev"

Query GBIF for Capra ibex

# Download data
ibex <- sp_occurrence("Capra", species = "ibex",
                            ext = ext(paradiso_boundary))

## 222 records found

## 0-222
## 222 records downloaded

ibex <- ibex[,c("lon", "lat")]
ibex <- ibex[complete.cases(ibex),]
# Convert to `sf`
ibex_points <- st_as_sf(ibex, coords = c("lon", "lat"), crs = "EPSG:4326")

Now extract elevation from SRTM at each Ibex siting location

ibex_elev <- extract(x = paradiso_srtm, y=vect(ibex_points))
ibex_points <- cbind(ibex_points, ibex_elev)
hist(ibex_points$Elev, breaks=20,
     main = "Distribution of Ibex sitings by elevation")

Prepare hill-shade raster

# Create hillshade raster
slope <-  terrain(paradiso_srtm, "slope", unit = "radians")
aspect <-  terrain(paradiso_srtm, "aspect", unit="radians")
hill_shade = shade(slope, aspect, angle=35, direction = 310)

Prepare a plot, including hillshade effect

tm_basemap("OpenStreetMap.Mapnik") +
  tm_shape(hill_shade) +
  tm_raster(alpha = 0.4, palette = "Greys",
            n=8, legend.show = FALSE) +
  tm_shape(paradiso_boundary) +
  tm_borders(col = "purple", lwd = 2) +
  tm_shape(ibex_points) + 
  tm_symbols(size=0.2, col = "red") 

• Acquire MODIS products
  • Time series
• Acquire species distribution from GBIF: https://www.gbif.org/dataset/search
• Acquire high resolution landcover data from ESA Worldcover: https://esa-worldcover.org/